similar to: Novice Questions

Dear Users, I try to calculate condition indexes and variance decomposition proportions in order to test for collinearity using colldiag() in perturb package, I got a large index and two variables with large variance decomposition proportions,but one of them is constant item.I also checked the VIF for that variable, the value is small.The result is as follows: Index intercept V1

Hi, and thanks for your help in order to do collinearity analysis I downloaded the perturb package. I run a lm (regression) and on that the ??calldiag?? commad to get condition numbers but i get the following message: the variable XY with modus ??numeric?? was not found (it does the same with all predictors despite all variables are numeric and exists). Can anyone tell me how can I go arround

Has anyone programmed condition indexes in R? I know that there is a function for variance inflation factors available in the car package; however, Belsley (1991) Conditioning Diagnostics (Wiley) notes that there are several weaknesses of VIFs: e.g. 1) High VIFs are sufficient but not necessary conditions for collinearity 2) VIFs don't diagnose the number of collinearities and 3) No one has

Hello R-user I want to compute a multiple regression but I would to include a check for collinearity of the variables. Therefore I would like to use a ridge regression. I tried lm.ridge() but I don't know yet how to get p-values (single Pr() and p of the whole model) out of this model. Can anybody tell me how to get a similar output like the summary(lm(...)) output? Or if there is

glm(A~B+C+D+E+F,family = binomial(link = "logit"),data=tre,na.action=na.omit) Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels however, glm(A~B+C+D+E,family = binomial(link = "logit"),data=tre,na.action=na.omit) runs fine glm(A~B+C+D+F,family = binomial(link =

Hi everyone, I'm trying to vectorize an application of lda to each 2D slice of a 3D array, but am running into trouble: It seems there are quite a few 2D slices that trigger either the "variables are collinear" warning, or worse, trigger a "variable appears to be constant within groups" error and fails (i.e., ceases computation rather than skips bad slice). There are

The lm() function in R seems to handle the inversion of singular X'X matrices (where there is collinearity between regression inputs) in a way where one of the inputs is dropped and this also seems to be the default behavior in SAS (please let me know if i'm wrong about this). In some other packages (i.e. octave ols() function) the pseudo inverse is computed where singular values less

Hello, I know there is plenty of people in this group who can give me a good answer :) I have a 2^k model where k=4 like this: Model 1) R~A*B*C*D If I use the "*" in R among all elements it means to me to explore all interactions and include them in the model i.e. I think this would be the so called 2-way anova. However, if I do this, it leads to model violations i.e. the

Is there a colinearty function implemented in R? I have tried help.search("colinearity") and help.search("collinearity") and have searched for "colinearity" and "collinearity" on http://www.rpad.org/Rpad/Rpad-refcard.pdf but with no success. Many thanks in advance, Peter Lauren.

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

Hi i need to create a model from 250 + variables with high collinearity, and only 17 data points (p = 250, n = 750). I would prefer to use Cp, AIC, and/or BIC to narrow down the number of variables, and then use VIF to choose a model without collinearity (if possible). I realize that having a huge p and small n is going to give me extreme linear dependency problems, but I *think* these model

Hello, in the paper "Avoiding the effects of concurvity in GAM's .." of Figueiras et al. (2003) it is mentioned that in GLM collinearity is taken into account in the calc of se but not in GAM (-> results in confidence interval too narrow, p-value understated, GAM S-Plus version). I haven't found any references to GAM and concurvity or collinearity on the R page. And I

I have a linear model y~x1+x2 of some data where the coefficient for x1 is higher than I would have expected from theory (0.7 vs 0.88) I wondered whether this would be an artifact due to x1 and x2 being correlated despite that the variance inflation factor is not too high (1.065): I used perturbation analysis to evaluate collinearity library(perturb)

Dear R users, I have a problem with multicollinearity in mixed models and I am using lmer in package lme4. From previous mailing list, I learn of a reply "http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg38537.html" which states that if not for interpretation but just for prediction, multicollinearity does not matter much. However, I am using mixed model to interpret something,

You are using a method that needs to estimate the covariance matrix of all the variables. If you have 80 variables, there are (80+1)*80/2 = 3240 variances and covariances to estimate. How many data points do you think you need to do that? Some people assume the covariance matrix is diagonal (i.e., assuming all the variables are uncorrelated). Even then you still have 80 variances to estimate.

Dear all, How can I test for collinearity in the predictor data set for multiple linear regression. Thanks Alex [[alternative HTML version deleted]]

Full_Name: David Teller Version: 1.7.1 OS: Windows XP Submission from: (NULL) (12.110.141.194) I've noticed several issues with the R memory management under Windows. It frequently runs out of memory, even when there is plenty of available memory on the system and even plenty of free process virtual address space. This tends to happen far more often when allocation a number of large objects

Dear R-experts, Searching the help archives I found a recommendation to do multivariate outlier identification by mahalanobis distances based on a robustly estimated covariance matrix and compare the resulting distances to a chi^2-distribution with p (number of your variables) degrees of freedom. I understand that compared to euclidean distances this has the advantage of being scale-invariant.

Hello all, I am trying to figure out an optimal linear model by using stepwise regression which requires partial f-test, I did some Googling on the Internet and realised that someone seemed to ask the question before: Jim Milks <jrclmilks at joimail.com> writes: > Dear all: > > I have a regression model that has collinearity problems (between > three regressor variables). I

Anna Crepes: Traubenzucker + Feldsalat spezielles Dressing (bringt selbst mit?) -------- Weitergeleitete Nachricht -------- Betreff: Fwd: Fwd: Fwd: Fwd: Fwd: Fwd: Fwd: 26. Datum: Thu, 11 Dec 2014 15:34:39 +0100 Von: Markus <universe at truemetal.org> An: universe at truemetal.org Geschenke Moritz: dunkle Schokolade. Geschenke Anna: normale Schokolade. -------- Weitergeleitete